Publications

    Deep Learning

  • Michael Chang, Tomer Ullman, Antonio Torralba, Joshua B. Tenenbaum:
    A Compositional Object-Based Approach To Learning Physical Dynamics (web) (bibtex)
    Proceedings of the 5th International Conference on Learning Representations
    #intuitive physics, #simulation, #deep learning, #unsupervised learning, #scene understanding
    @article{MichaelChang:2017:07f51,
    author = {Michael Chang and Tomer Ullman and Antonio Torralba and Joshua B. Tenenbaum},
    journal = {Proceedings of the 5th International Conference on Learning Representations},
    title = {A Compositional Object-Based Approach To Learning Physical Dynamics},
    year = {2017},
    keywords = {intuitive physics, simulation, deep learning, unsupervised learning, scene understanding},
    doi = {},
    url = {https://arxiv.org/abs/1612.00341}
    }
  • Renqiao Zhang, Jiajun Wu, Chengkai Zhang, William T. Freeman, Joshua B. Tenenbaum:
    A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding (web) (bibtex)
    Proceedings of the 38th Annual Conference of the Cognitive Science Society
    #intuitive physics, #simulation, #deep learning, #scene understanding
    @article{RenqiaoZhang:2016:84ee7,
    author = {Renqiao Zhang and Jiajun Wu and Chengkai Zhang and William T. Freeman and Joshua B. Tenenbaum},
    journal = {Proceedings of the 38th Annual Conference of the Cognitive Science Society},
    title = {A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding},
    year = {2016},
    keywords = {intuitive physics, simulation, deep learning, scene understanding},
    doi = {},
    url = {http://blocks.csail.mit.edu/}
    }
  • Jiajun Wu, Joseph J. Lim, Hongyi Zhang, Joshua B. Tenenbaum, William T. Freeman:
    Physics 101: Learning Physical Object Properties from Unlabeled Videos (web) (bibtex)
    British Machine Vision Conference (BMVC)
    #intuitive physics, #unsupervised learning, #deep learning, #scene understanding
    @article{JiajunWu:2016:9e6c9,
    author = {Jiajun Wu and Joseph J. Lim and Hongyi Zhang and Joshua B. Tenenbaum and William T. Freeman},
    journal = {British Machine Vision Conference (BMVC)},
    title = {Physics 101: Learning Physical Object Properties from Unlabeled Videos},
    year = {2016},
    keywords = {intuitive physics, unsupervised learning, deep learning, scene understanding},
    doi = {},
    url = {http://phys101.csail.mit.edu/}
    }
  • Jiajun Wu, Ilker Yildirim, Joseph J. Lim, William T. Freeman, Joshua B. Tenenbaum:
    Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning (web) (bibtex)
    Advances in Neural Information Processing Systems (NIPS)
    #intuitive physics, #simulation, #deep learning, #unsupervised learning, #scene understanding
    @article{JiajunWu:2015:27dc4,
    author = {Jiajun Wu and Ilker Yildirim and Joseph J. Lim and William T. Freeman and Joshua B. Tenenbaum},
    journal = {Advances in Neural Information Processing Systems (NIPS)},
    title = {Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning},
    year = {2015},
    keywords = {intuitive physics, simulation, deep learning, unsupervised learning, scene understanding},
    doi = {},
    url = {http://galileo.csail.mit.edu/}
    }
  • Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman:
    Single Image 3D Interpreter Network (web) (bibtex)
    European Conference in Computer Vision (ECCV)
    #deep learning, #self-supervised learning, #3d vision
    @article{JiajunWu:2016:770f7,
    author = {Jiajun Wu and Tianfan Xue and Joseph J. Lim and Yuandong Tian and Joshua B. Tenenbaum and Antonio Torralba and William T. Freeman},
    journal = {European Conference in Computer Vision (ECCV)},
    title = {Single Image 3D Interpreter Network},
    year = {2016},
    keywords = {deep learning, self-supervised learning, 3d vision},
    doi = {},
    url = {http://3dinterpreter.csail.mit.edu/}
    }
  • Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, Joshua B. Tenenbaum:
    Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (web) (bibtex)
    Advances in Neural Information Processing Systems (NIPS)
    #simulation, #deep learning, #generative adversarial learning, #3d vision
    @article{JiajunWu:2016:3471d,
    author = {Jiajun Wu and Chengkai Zhang and Tianfan Xue and William T. Freeman and Joshua B. Tenenbaum},
    journal = {Advances in Neural Information Processing Systems (NIPS)},
    title = {Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling},
    year = {2016},
    keywords = {simulation, deep learning, generative adversarial learning, 3d vision},
    doi = {},
    url = {http://3dgan.csail.mit.edu/}
    }
  • Zhoutong Zhang, Jiajun Wu, Qiujia Li, Zhengjia Huang, James Traer, Josh H. McDermott, Joshua B. Tenenbaum, William T. Freeman:
    Generative Modeling of Audible Shapes for Object Perception (pdf) (bibtex)
    IEEE International Conference on Computer Vision (ICCV)
    #deep learning, #simulation, #auditory perception, #scene understanding
    @article{ZhoutongZhang:2017:4f1fd,
    author = {Zhoutong Zhang and Jiajun Wu and Qiujia Li and Zhengjia Huang and James Traer and Josh H. McDermott and Joshua B. Tenenbaum and William T. Freeman},
    journal = {IEEE International Conference on Computer Vision (ICCV)},
    title = {Generative Modeling of Audible Shapes for Object Perception},
    year = {2017},
    keywords = {deep learning, simulation, auditory perception, scene understanding},
    doi = {},
    url = {https://jiajunwu.com/papers/gensound_iccv.pdf}
    }
  • Jiajun Wu, Joshua B. Tenenbaum, Pushmeet Kohli:
    Neural Scene De-rendering (web) (bibtex)
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    #deep learning, #self-supervised learning, #inverse graphics, #computer vision, #scene understanding
    @article{JiajunWu:2017:2afa9,
    author = {Jiajun Wu and Joshua B. Tenenbaum and Pushmeet Kohli},
    journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    title = {Neural Scene De-rendering},
    year = {2017},
    keywords = {deep learning, self-supervised learning, inverse graphics, computer vision, scene understanding},
    doi = {},
    url = {http://nsd.csail.mit.edu/}
    }
  • Jiajun Wu, Erika Lu, Pushmeet Kohli, William T. Freeman, Joshua B. Tenenbaum:
    Learning to See Physics via Visual De-animation (web) (bibtex)
    Advances in Neural Information Processing Systems (NIPS)
    #intuitive physics, #simulation, #deep learning, #scene understanding
    @article{JiajunWu:2017:4849f,
    author = {Jiajun Wu and Erika Lu and Pushmeet Kohli and William T. Freeman and Joshua B. Tenenbaum},
    journal = {Advances in Neural Information Processing Systems (NIPS)},
    title = {Learning to See Physics via Visual De-animation},
    year = {2017},
    keywords = {intuitive physics, simulation, deep learning, scene understanding},
    doi = {},
    url = {http://vda.csail.mit.edu/}
    }
  • Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, William T. Freeman, Joshua B. Tenenbaum:
    MarrNet: 3D Shape Reconstruction via 2.5D Sketches (web) (bibtex)
    Advances in Neural Information Processing Systems (NIPS)
    #3d vision, #deep learning
    @article{JiajunWu:2017:cfe2b,
    author = {Jiajun Wu and Yifan Wang and Tianfan Xue and Xingyuan Sun and William T. Freeman and Joshua B. Tenenbaum},
    journal = {Advances in Neural Information Processing Systems (NIPS)},
    title = {MarrNet: 3D Shape Reconstruction via 2.5D Sketches},
    year = {2017},
    keywords = {3d vision, deep learning},
    doi = {},
    url = {http://marrnet.csail.mit.edu/}
    }
  • Michael Janner, Jiajun Wu, Tejas D. Kulkarni, Ilker Yildirim, Joshua B. Tenenbaum:
    Self-Supervised Intrinsic Image Decomposition (web) (bibtex)
    Advances in Neural Information Processing Systems (NIPS)
    #computer vision, #deep learning, #self-supervised learning
    @article{MichaelJanner:2017:e02af,
    author = {Michael Janner and Jiajun Wu and Tejas D. Kulkarni and Ilker Yildirim and Joshua B. Tenenbaum},
    journal = {Advances in Neural Information Processing Systems (NIPS)},
    title = {Self-Supervised Intrinsic Image Decomposition},
    year = {2017},
    keywords = {computer vision, deep learning, self-supervised learning},
    doi = {},
    url = {http://rin.csail.mit.edu/}
    }
  • Zhoutong Zhang, Qiujia Li, Zhengjia Huang, Jiajun Wu, Joshua B. Tenenbaum, William T. Freeman:
    Shape and Material from Sound (web) (bibtex)
    Advances in Neural Information Processing Systems (NIPS)
    #auditory perception, #deep learning, #simulation
    @article{ZhoutongZhang:2017:1d3c7,
    author = {Zhoutong Zhang and Qiujia Li and Zhengjia Huang and Jiajun Wu and Joshua B. Tenenbaum and William T. Freeman},
    journal = {Advances in Neural Information Processing Systems (NIPS)},
    title = {Shape and Material from Sound},
    year = {2017},
    keywords = {auditory perception, deep learning, simulation},
    doi = {},
    url = {http://sound.csail.mit.edu/}
    }
  • Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, Josh Tenenbaum:
    Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation (web) (bibtex)
    Advances in Neural Information Processing Systems (NIPS)
    #reinforcement learning, #hierarchical modeling, #deep learning
    @article{TejasDKulkarni:2016:847fe,
    author = {Tejas D Kulkarni and Karthik Narasimhan and Ardavan Saeedi and Josh Tenenbaum},
    journal = {Advances in Neural Information Processing Systems (NIPS)},
    title = {Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation},
    year = {2016},
    keywords = {reinforcement learning, hierarchical modeling, deep learning},
    doi = {},
    url = {http://papers.nips.cc/paper/6232-hierarchical-deep-reinforcement-learning-integrating-temporal-abstraction-and-intrinsic-motivation}
    }
  • Tejas D Kulkarni, William F Whitney, Pushmeet Kohli, Josh Tenenbaum:
    Deep Convolutional Inverse Graphics Network (web) (bibtex)
    Advances in Neural Information Processing Systems (NIPS)
    #inverse vision, #deep learning, #disentangled representation,
    @article{TejasDKulkarni:2015:19456,
    author = {Tejas D Kulkarni and William F Whitney and Pushmeet Kohli and Josh Tenenbaum},
    journal = {Advances in Neural Information Processing Systems (NIPS)},
    title = {Deep Convolutional Inverse Graphics Network},
    year = {2015},
    keywords = {inverse vision, deep learning, disentangled representation,},
    doi = {},
    url = {http://papers.nips.cc/paper/5851-deep-convolutional-inverse-graphics-network}
    }
  • Tejas D Kulkarni, Pushmeet Kohli, Joshua B Tenenbaum, Vikash Mansinghka:
    Picture : A Probabilistic Programming Language for Scene Perception (pdf) (doi) (bibtex)
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
    #analysis-by-synthesis, #inference, #deep learning, #inverse vision, #3d vision, #probabilistic programming
    @article{TejasDKulkarni:2015:cf27d,
    author = {Tejas D Kulkarni and Pushmeet Kohli and Joshua B Tenenbaum and Vikash Mansinghka},
    journal = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    title = {Picture : A Probabilistic Programming Language for Scene Perception},
    year = {2015},
    keywords = {analysis-by-synthesis, inference, deep learning, inverse vision, 3d vision, probabilistic programming},
    doi = {10.1109/CVPR.2015.7299068},
    url = {http://openaccess.thecvf.com/content_cvpr_2015/papers/Kulkarni_Picture_A_Probabilistic_2015_CVPR_paper.pdf}
    }
  • Amir Arsalan Soltani, Haibin Huang, Jiajun Wu, Tejas D Kulkarni, Joshua B Tenenbaum:
    Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative (web) (doi) (bibtex)
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
    #2d to 3d, #3d vision, #3d generation, #3d reconstruction, #inverse vision, #scene understanding, #deep learning
    @article{AmirArsalanSoltani:2017:11a5c,
    author = {Amir Arsalan Soltani and Haibin Huang and Jiajun Wu and Tejas D Kulkarni and Joshua B Tenenbaum},
    journal = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    title = {Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative },
    year = {2017},
    keywords = {2d to 3d, 3d vision, 3d generation, 3d reconstruction, inverse vision, scene understanding, deep learning},
    doi = {10.1109/CVPR.2017.269},
    url = {https://github.com/Amir-Arsalan/Synthesize3DviaDepthOrSil}
    }

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